block truncation coding (BTC)

Block Truncation Coding (BTC): A Simple and Efficient Image Compression Technique

Block Truncation Coding (BTC) is a lossy image compression technique that leverages a straightforward approach to reduce image data while preserving key visual information. It achieves this by segmenting the image into non-overlapping blocks of pixels and then applying a simple two-level quantization scheme to each block. This article will delve into the working principles of BTC, highlighting its advantages and limitations.

The Core Mechanism of BTC:

1. Image Segmentation: The input image is divided into blocks of equal size, typically n x n pixels.

2. Block Processing: Each block undergoes independent processing. The average pixel value (mean) and the standard deviation are calculated. These values represent the block's overall brightness and contrast, respectively.

3. Two-Level Quantization: A two-level quantizer is then applied to each block, where pixels are assigned one of two reconstruction levels:

   • Level 1: For pixels with values greater than or equal to the block's mean.
   • Level 2: For pixels with values less than the mean.

4. Bit Map Representation: A binary bit map, the size of the original block (n x n), is generated. Each bit in the map corresponds to a pixel in the block. A "1" indicates that the pixel is assigned Level 1, while a "0" indicates Level 2.

5. Encoding: The encoded representation of the image consists of the block mean, standard deviation, and the bit map for each block.

6. Decoding: Reconstruction is straightforward. The bit map is used to assign the appropriate reconstruction level (mean + standard deviation/2 for Level 1, mean - standard deviation/2 for Level 2) to each pixel within the block.

Advantages of BTC:

• Simplicity: BTC's algorithm is relatively easy to implement, requiring minimal computational resources.

• Low Complexity: Encoding and decoding operations are fast due to the use of simple arithmetic operations and lookup tables.

• Good Performance: BTC provides a reasonable compression ratio while maintaining acceptable image quality, particularly for images with high spatial correlation.

Limitations of BTC:

• Lossy Compression: BTC is a lossy compression technique, meaning some information is lost during the quantization process, leading to visual artifacts, particularly at edges and fine details.

• Blockiness Artifacts: The segmentation into blocks can lead to visible block boundaries, particularly when compression ratios are high.

• Limited Color Support: Traditional BTC is designed for grayscale images and requires modifications to handle color images.

Applications of BTC:

Despite its limitations, BTC has found applications in various areas:

• Medical Imaging: In applications like medical image transmission and storage, its low complexity and acceptable quality make it suitable.

• Remote Sensing: For transmitting images from satellites or drones, BTC offers a good balance between compression and image quality.

• Document Imaging: BTC's simplicity and speed make it suitable for compressing document images.

Conclusion:

BTC is a compelling lossy compression technique known for its simplicity, low computational cost, and acceptable image quality. While it faces limitations like blockiness artifacts and loss of details, its advantages make it suitable for various applications where efficient compression and low computational complexity are paramount. Advancements in BTC have led to modifications like adaptive quantization and improved block size selection, further enhancing its performance.